Moments ago at NVIDIA’s SIGGRAPH 2018 keynote presentation, company CEO Jensen Huang formally unveiled the company’s much awaited (and much rumored) Turing GPU architecture. The next generation of NVIDIA’s GPU designs, Turing will be incorporating a number of new features and is rolling out this year. While the focus of today’s announcements is on the professional visualization (ProViz) side of matters, we expect to see this used in other upcoming NVIDIA products as well. And by the same token, today’s reveal should not be considered an exhaustive listing of all of Turing’s features.

Hybrid Rendering & Neural Networking: RT & Tensor Cores
So what does Turing bring to the table? The marquee feature, at least for NVIDIA’s ProViz crowd, is on hybrid rendering, which combines ray tracing with traditional rasterization to exploit the strengths of both technologies. This announcement is essentially a continuation of NVIDIA’s RTX announcement from earlier this year, so if you thought that announcement was a little sparse, well then here is the rest of the story.

The big change here is that NVIDIA is going to be including even more ray tracing hardware with Turing in order to offer faster and more efficient hardware ray tracing acceleration. New to the Turing architecture is what NVIDIA is calling an RT core, the underpinnings of which we aren’t fully informed on at this time, but serve as dedicated ray tracing processors. These processor blocks accelerate both ray-triangle intersection checks and bounding volume hierarchy (BVH) manipulation, the latter being a very popular data structure for storing objects for ray tracing.

NVIDIA is stating that the fastest Turing parts can cast 10 Billion (Giga) rays per second, which compared to the unaccelerated Pascal is a 25x improvement in ray tracing performance.

The Turing architecture also carries over the tensor cores from Volta, and indeed these have even been enhanced over Volta. The tensor cores are an important aspect of multiple NVIDIA initiatives. Along with speeding up ray tracing itself, NVIDIA’s other tool in their bag of tricks is to reduce the amount of rays required in a scene by using AI denoising to clean up an image, which is something the tensor cores excel at. Of course that’s not the only feature tensor cores are for – NVIDIA’s entire AI/neural networking empire is all but built on them – so while not a primary focus for the SIGGRAPH crowd, this also confirms that NVIDIA’s most powerful neural networking hardware will be coming to a wider range of GPUs.

New to Turing is support for a wider range of precisions, and as such the potential for significant speedups in workloads that don’t require high precisions. On top of Volta’s FP16 precision mode, Turing’s tensor cores also support INT8 and even INT4 precisions. These are 2x and 4x faster than FP16 respectively, and while NVIDIA’s presentation doesn’t dive too deep here, I would imagine they’re doing something similar to the data packing they use for low-precision operations on the CUDA cores. And without going too deep ourselves here, while reducing the precision of a neural network has diminishing returns – by INT4 we’re down to a total of just 16(!) values – there are certain models that really can get away with this very low level of precision. And as a result the lower precision modes, while not always useful, will undoubtedly make some users quite happy at the throughput, especially in inferencing tasks.

Getting back to hybrid rendering in general though, it’s interesting that despite these individual speed-ups, NVIDIA’s overall performance promises aren’t quite as extreme. All told, the company is promising a 6x performance boost versus Pascal, and this doesn’t specify against which parts. Time will tell if even this is a realistic assessment, as even with the RT cores, ray tracing in general is still quite the resource hog.

Meanwhile, to better take advantage of the tensor cores outside of ray tracing and specialty deep learning software, NVIDIA will be rolling out a SDK, NVIDIA NGX, to integrate neural networking into image processing. Details here are sparse, but NVIDIA is envisioning using neural networking and the tensor cores for additional image and video processing, including methods like the upcoming Deep Learning Anti-Aliasing (DLAA).